Belgrade, Serbia
Belgrade, Serbia

The Megatrend University is a private university located in Belgrade, Serbia. Megatrend Business school, which later became Megatrend University, was founded in 1989. In an article about problems in Serbian higher education, Al Jazeera described Megatrend as "essentially a degree mill where diplomas can be obtained for cash." Wikipedia.


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Brajevic I.,University of Belgrade | Tuba M.,Megatrend University
Journal of Intelligent Manufacturing | Year: 2013

Artificial bee colony (ABC) algorithm developed by Karaboga is a nature inspired metaheuristic based on honey bee foraging behavior. It was successfully applied to continuous unconstrained optimization problems and later it was extended to constrained design problems as well. This paper introduces an upgraded artificial bee colony (UABC) algorithm for constrained optimization problems. Our UABC algorithm enhances fine-tuning characteristics of the modification rate parameter and employs modified scout bee phase of the ABC algorithm. This upgraded algorithm has been implemented and tested on standard engineering benchmark problems and the performance was compared to the performance of the latest Akay and Karaboga's ABC algorithm. Our numerical results show that the proposed UABC algorithm produces better or equal best and average solutions in less evaluations in all cases. © 2012 Springer Science+Business Media, LLC.


Jovanovic R.,Institute of Physics Belgrade | Tuba M.,Megatrend University
Applied Soft Computing Journal | Year: 2011

The minimum weight vertex cover problem is an interesting and applicable NP-hard problem that has been investigated from many different aspects. The ant colony optimization metaheuristic is a relatively new technique that was successfully adjusted and applied to many hard combinatorial optimization problems, including the minimum weight vertex cover problem. Some kind of hybridization or exploitation of the knowledge about specific problem often greatly improves the performance of standard evolutionary algorithms. In this article we propose a pheromone correction heuristic strategy that uses information about the best-found solution to exclude suspicious elements from it. Elements are suspicious if they have some undesirable properties that make them unlikely members of the optimal solution. This hybridization improves pure ant colony optimization algorithm by avoiding early trapping in local convergence. We tested our algorithm on numerous test-cases that were used in the previous research of the same problem and our algorithm uniformly performed better, giving slightly better results in significantly shorter time. © 2011 Elsevier B.V. All rights reserved.


Alihodzic A.,University of Sarajevo | Tuba M.,Megatrend University
Scientific World Journal | Year: 2014

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem.The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. Copyright © 2014 A. Alihodzic and M. Tuba.


Jovanovic R.,Texas A&M University at Qatar | Tuba M.,Megatrend University
Computer Science and Information Systems | Year: 2013

In this paper an ant colony optimization (ACO) algorithm for the minimum connected dominating set problem (MCDSP) is presented. The MCDSP become increasingly important in recent years due to its applicability to the mobile ad hoc networks (MANETs) and sensor grids. We have implemented a one-step ACO algorithm based on a known simple greedy algorithm that has a significant drawback of being easily trapped in local optima. We have shown that by adding a pheromone correction strategy and dedicating special attention to the initial condition of the ACO algorithm this negative effect can be avoided. Using this approach it is possible to achieve good results without using the complex two-step ACO algorithm previously developed. We have tested our method on standard benchmark data and shown that it is competitive to the existing algorithms.


Tuba M.,Megatrend University | Jovanovic R.,Texas A&M University at Qatar
International Journal of Computers, Communications and Control | Year: 2013

A new, improved ant colony optimization algorithm with novel pheromone correction strategy is introduced. It is implemented and tested on the traveling salesman problem. Algorithm modification is based on undesirability of some elements of the current best found solution. The pheromone values for highly undesirable links are significantly lowered by this a posteriori heuristic. This new hybridized algorithm with the strategy for avoiding stagnation by leaving local optima was tested on standard benchmark problems from the TSPLIB library and superiority of our method to the basic ant colony optimization and also to the particle swarm optimization is shown. The best found solutions are improved, as well as the mean values for multiple runs. The computation cost increase for our modification is negligible. © 2006-2013 by CCC Publications.


Tuba M.,Megatrend University
Entropy | Year: 2013

Maximum entropy method has been successfully used for underdetermined systems. Network design problem, with routing and topology subproblems, is an underdetermined system and a good candidate for maximum entropy method application. Wireless ad-hoc networks with rapidly changing topology and link quality, where the speed of recalculation is of crucial importance, have been recently successfully investigated by maximum entropy method application. In this paper we prove a theorem that establishes asymptotic properties of the maximum entropy routing solution. This result, besides being theoretically interesting, can be used to direct initial approximation for iterative optimization algorithms and to speed up their convergence. © 2013 by the author.


Subotic M.,Megatrend University
Proceedings of the European Computing Conference, ECC '11 | Year: 2011

In this paper we present a modification of artificial bee colony (ABC) algorithm for constrained optimization problems. In nature more than one onlooker bee goes to a promising food source reported by employed bee. Our proposed modification forms a mutant solution in onlooker phase using three onlookers. This approach obtains better results than the original artificial bee colony algorithm. Our multiple onlooker modified algorithm was tested on the full set of 24 well known benchmark functions known as g-functions and proved to be superior to the pure ABC algorithm in most cases.


Bacanin N.,Megatrend University
International Journal of Mathematics and Computers in Simulation | Year: 2012

Evolutionary computation (EC) algorithms have been successfully applied to hard optimization problems. In this very active research area one of the newest EC algorithms is a cuckoo search (CS) metaheuristic for unconstrained optimization problems which was developed by Yang and Deb in MATLAB software. This paper presents our software implementation of CS algorithm which we called CSApp. CSApp is an object-oriented system which is fast, robust, scalable and error prone. User friendly graphical user interface (GUI) enables simple adjustment of algorithm's control parameters. The system was successfully tested on standard benchmark functions for unconstrained problems with various number of parameters. CSApp software, as well as experimental results are presented in this paper.


Bacanin N.,Megatrend University
Proceedings of the European Computing Conference, ECC '11 | Year: 2011

This paper presents an object-oriented software system that implements a cuckoo search (CS) metaheuristic for unconstrained optimization problems. Yang and Deb developed cuckoo search algorithm in MATLAB and tested it on some standard benchmark functions as well as on some engineering optimization problems where it showed promising results. We developed our algorithm in JAVA programming language which is faster and easier for maintenance since it is object-oriented. The application includes user friendly graphical user interface (GUI) and it was successfully tested on standard benchmark functions for unconstrained problems.


Subotic M.,Megatrend University | Tuba M.,Megatrend University
Studies in Informatics and Control | Year: 2014

Swarm intelligence metaheuristics have been successfully used for hard optimization problems. After the initial introduction phase such algorithms are further improved by modifications and hybridizations. Parallelization is usually introduced for performance improvement and better resources utilization. In this paper we present an improved parallelized artificial bee colony (ABC) algorithm with multiple swarm inter-communication and learning that not only significantly improves computational time, but also improves the results. Proposed algorithm was tested on large set of standard benchmark functions and it outperformed the state-of-art ABC algorithm.

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